Decision support for psychiatric diagnosis based on a simple questionnaire.
This paper compares two classifiers: Pseudo Bayesian and Neural Network for assisting in making diagnoses of psychiatric patients based on a simple yes/no questionnaire which is provided at the outpatient's first visit to the hospital. The classifiers categorize patients into three most commonly seen ICD classes, i.e. schizophrenic, emotional and neurotic disorders. One hundred completed questionnaires were utilized for constructing and evaluating the classifiers. Average correct decision rates were 73.3% for the Pseudo Bayesian Classifier and 77.3% for the Neural Network classifier. These rates were higher than the rate which an experienced psychiatrist achieved based on the same restricted data as the classifiers utilized. These classifiers may be effectively utilized for assisting psychiatrists in making their final diagnoses.
Yana
K
K
Department of Electronic Informatics, Hosei University, Tokyo, Japan. kyana@bme.ei.hosei.ac.jp
Mizuta
H
H
Kawachi
K
K
Yoshida
H
H
Iida
K
K
Okubo
Y
Y
Tohru
M
M
Okuyama
F
F
D000342:Affective Symptoms / Q000175:diagnosis; D001499:Bayes Theorem; D020000:Decision Support Systems, Clinical; D006801:Humans; D001523:Mental Disorders / Q000175:diagnosis*; D016571:Neural Networks, Computer; D009497:Neurotic Disorders / Q000175:diagnosis; D012559:Schizophrenia / Q000175:diagnosis; D011795:Surveys and Questionnaires